Connect to Catmaid Server

import pymaid

source = pymaid.CatmaidInstance('https://fafb.catmaid.virtualflybrain.org/' , api_token=None) #'9944b09199c62bcf9418ad846dd0e4bbdfc6ee4b'

INFO  : Global CATMAID instance set. Caching is ON. (pymaid)

Retrieve Annotations

neurons = pymaid.get_annotated("Paper: Zheng et al 2018") # Paper of interest

INFO  : Searching for: Paper: Zheng et al 2018 (pymaid)

neurons

	id	name	type	skeleton_ids
0	17	Uniglomerular mALT VA6 adPN 017 DB	neuron	[16]
1	7287	KCa'B'ap 7287 EAM	neuron	[7286]
2	7647	KCa'B'm 7647 LK	neuron	[7646]
3	9579	KCy 9579 EAM	neuron	[9578]
4	11743	KCa'B'm 11743 BAK	neuron	[11742]
...	...	...	...	...
140	1775707	Uniglomerular mALT DL1 adPN 22153 JMR	neuron	[1775706]
141	1785035	Uniglomerular mALT DA2 lPN 57399 LK	neuron	[1785034]
142	3104791	KCy 16943 BH	neuron	[3104789]
143	3445526	KCy 6487 ML-NS	neuron	[3445525]
144	14003784	Uniglomerular mALT VP5 + SEZ adPN 57472 LK ECM	neuron	[14003783]

145 rows × 4 columns

Get Neuron IDs in a List for Indexing

neuron_ids = [j for j in neurons["skeleton_ids"]] # Get neuron IDs

old = neuron_ids
neuron_ids = []

for i in range(len(old)):
    neuron_ids.append(old[i][0])

Import Connectome Options

Adjacency Matrix Between Source and Target Neurons

connectome = pymaid.adjacency_matrix(neuron_ids)
connectome = connectome.to_numpy()

from graspologic.plot import heatmap
heatmap(connectome)

<AxesSubplot:>

Adjacency Matrix From Neurons' Connectors

pymaid.adjacency_from_connectors is meant to generate an adjacency matrix from data with repeated skeleton ids. Our IDs do not repeat so this was skipped.

Edges Between Sets of Neurons

pymaid.get_edges(neuron_ids)

	source	target	weight
0	203840	16	13
1	203840	7286	4
2	203840	7646	11
3	203840	9578	20
4	203840	11742	6
...	...	...	...
826	57385	57418	1
827	57385	60445	1
828	57385	1785034	1
829	46105	57503	2
830	46105	14003783	1

831 rows × 3 columns

This is just the same as the original connectome imported earlier, as shown below:

len(connectome[connectome != 0])

831

Partners connected by synapses, gap junctions, or attachments

pymaid.get_partners(neuron_ids)

INFO  : Fetching connectivity table for 145 neurons (pymaid)

---------------------------------------------------------------------------
HTTPError                                 Traceback (most recent call last)
<ipython-input-8-a21d9a28a1fc> in <module>
----> 1 pymaid.get_partners(neuron_ids)

~/anaconda3/envs/NDD/lib/python3.7/site-packages/pymaid/cache.py in wrapper(*args, **kwargs)
    314         try:
    315             # Execute function
--> 316             res = function(*args, **kwargs)
    317         except BaseException:
    318             # If error was raised, remove new entries from cache

~/anaconda3/envs/NDD/lib/python3.7/site-packages/pymaid/fetch.py in get_partners(x, threshold, min_size, filt, min_confidence, directions, remote_instance)
    757     # Get neurons' names
    758     names = get_names([n for d in connectivity_data for n in connectivity_data[
--> 759                       d]] + list(x), remote_instance=remote_instance)
    760 
    761     df = pd.DataFrame(columns=['neuron_name', 'skeleton_id',

~/anaconda3/envs/NDD/lib/python3.7/site-packages/pymaid/cache.py in wrapper(*args, **kwargs)
    314         try:
    315             # Execute function
--> 316             res = function(*args, **kwargs)
    317         except BaseException:
    318             # If error was raised, remove new entries from cache

~/anaconda3/envs/NDD/lib/python3.7/site-packages/pymaid/fetch.py in get_names(x, remote_instance)
    851         get_names_postdata[key] = x[i]
    852 
--> 853     names = remote_instance.fetch(remote_get_names_url, post=get_names_postdata)
    854 
    855     logger.debug('Names for {} of {} skeleton IDs retrieved'.format(len(names),

~/anaconda3/envs/NDD/lib/python3.7/site-packages/pymaid/client.py in fetch(self, url, post, files, on_error, desc, disable_pbar, leave_pbar, return_type)
    472             if on_error == 'raise':
    473                 raise HTTPError('{} errors encountered: {}'.format(len(errors),
--> 474                                                                    '\n'.join(errors)))
    475             else:
    476                 for e, d in zip(errors, details):

HTTPError: 1 errors encountered: 413 Server Error: Request Entity Too Large for url: https://fafb.catmaid.virtualflybrain.org/1/skeleton/neuronnames

# DO NOT DELETE THIS: IT EXPORTS FILE TO HTML

!jupyter nbconvert --to html pymaid_import.ipynb

[NbConvertApp] Converting notebook pymaid_import.ipynb to html
[NbConvertApp] Writing 583988 bytes to pymaid_import.html